I am using sklearn to train some models (random forest, decision tree). For the training I am using RandomsearchCV with Stratified k-fold as cross-validation. Then I make a predictions on the test set and calculate the test score.
However, I would like to compare the test score with the training score. I assumed I could use the mean_train_score of the cv_results_ report from the RandomseachCV as training score for the model, because I thought it would show the validation against the hould-out-fold from the k-folds. However, I am not sure about this because there is also a mean_test_score.
I was looking for an explanation of the mean_train_score and mean_test_score. I know these scores exits also for the single folds. But how are these scores calculated? And is one of them my training score, which shows how my model during the training performed?
I found an approach of explanation, but it's too superficial for me: GridSearch mean_test_score vs mean_train_score